com.weighscore.neuro
Class Signal
- public class Signal
- The class which performs the network asking process and error back propagation. It is instantiated for every ask, teach or test action. The system calls go method when asking the network. The teacher may call goBack method to back propagat the error and to get gradient
- Version:
- 2.0
- Author:
- Fyodor Kravchenko
getGoneForth
public boolean getGoneForth()
- Tests if the signal was sent forth and reached its end
- Returns:
- true if the signal was sent forth and reached it's destination
getGoneBack
public boolean getGoneBack()
- Tests if the signal was sent back and reached its end
- Returns:
- true if the signal was sent back and reached it's destination
getAnswer
public double getAnswer(com.weighscore.neuro.Neuron n)
- Gets the output of the specified neuron
- Parameters:
n
- Neuron
- Returns:
- output of the neuron
getError
public double getError(com.weighscore.neuro.Neuron n)
- Gets the back propagated error for the specified neuron
- Parameters:
n
- Neuron
- Returns:
- double
getGradient
public double getGradient(com.weighscore.neuro.WeightHolder wh)
- Gets the fitness function gradient member for the specified neuron
- Parameters:
wh
- Neuron or synapse
- Returns:
- gradient member
getGradient
public double[][] getGradient()
- Returns the gradient values that are computed while error back propagation process
- Returns:
- The gradient members as an array of arrays of doubles. The array size equals to the quantity of neurons in the network. Every array entry is an array, which size equals to the quantity of the neuron's input sysnapses plus one; the first array entry corresponds to the neuron's theshold
go
public double[] go(double[] question)
- Propagate the question by the neural network
- Parameters:
question
- the question (input values)
- Returns:
- the answer (the output values)
goBack
public double[][] goBack(double[] error)
- Propagate the error back computing the gradient
- Parameters:
error
- error (the difference between the given answer and the correct answer)
- Returns:
- the gradient